| 摘要: |
| 针对基于拟合曲线的数据预测机制在WSN数据收集应用中区间敏感的问题,提出了基于时间周期的拟合曲线相似度序列,将基于预测的数据收集问题转化为一定精度下预测相似度的估计问题.基于特征相似度服从高斯分布的假定,研究准确预测感知相似度的最大概率,采用贪婪算法动态调整预测相似度.最后,采用PSO算法实现基于预测相似度的预测数据推断.仿真实验结果表明,该算法达到了预期效果,在能耗方面有较大的提高. |
| 关键词: 曲线相似度 拟合 无线传感器网络 数据预测 |
| DOI： |
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| 基金项目:湖南省教育厅资助重点项目(14A004);湖南省教育厅资助科研项目(13C1022) |
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| Algorithm of WSN Data Collection Based on Similarity Prediction |
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LI Ping1, YANG Wu1, WU Jia-Ying1, HU Hai-Luo2
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1.School of Computer and Telecommunications, Changsha University of Science and Technology, Changsha 410114, China;2.Mid-South Design and Research Institute, Changsha 410014, China
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| Abstract: |
| To tackle the issue of interval sensitivity in the application of WSN data collection based on the prediction mechanism of fitting curve, this paper proposes fitting curve similarity sequence based on time periods to transform data collection problem based on prediction into the similarity estimation under certain accuracy. Feature-Based similarity is assumed to obey the Gaussian distribution. By studying the maximum probability of accurate prediction of perceived similarity, the proposed method uses Greedy algorithm to dynamically adjust the predicted similarity. Finally, it uses PSO algorithm to achieve inference of the predicted data based on predicted similarity. Simulation results show that this algorithm has achieved the desired results, and also provides great improvement in terms of energy consumption. |
| Key words: curve similarity fitting wireless sensor network (WSN) data prediction |
